Papers with abstractive text summarisation

2 papers
Deep Copycat Networks for Text-to-Text Generation (D19-1)

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Challenge: Text-to-text generation tasks require copying words from the input to the output.
Approach: They propose a transformer-based pointer network for text-to-text generation which generates more abstractive summaries and a further extension of this architecture for automatic post-editing.
Outcome: The proposed model outperforms existing models in text-to-text generation tasks and improves translation accuracy.
LongDocFACTScore: Evaluating the Factuality of Long Document Abstractive Summarisation (2024.lrec-main)

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Challenge: Existing metrics for text summarisation have restrictive token limits, limiting their effectiveness.
Approach: They propose a human-annotated data set for evaluating automatic factuality metrics . they propose 'longDocFACTScore' framework which can be extended to any length document .
Outcome: The proposed framework outperforms state-of-the-art metrics in evaluating long document summarisation data sets.

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